paddlenlp.transformers.xlnet.tokenizer 源代码

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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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"""Tokenization class for XLNet model."""

import os
import unicodedata
from shutil import copyfile
from typing import List, Optional

import sentencepiece as spm

from .. import PretrainedTokenizer, AddedToken

__all__ = ['XLNetTokenizer']

SENTENCEPIECE_UNDERLINE = "▁"
SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4


[文档]class XLNetTokenizer(PretrainedTokenizer): """ Constructs an XLNet tokenizer based on `SentencePiece <https://github.com/google/sentencepiece>`__. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file (ends with '.spm') required to instantiate a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer. do_lower_case (bool, optional): Whether or not to lowercase the input when tokenizing. Defaults to `False` and **does not** lowercase the input. remove_space (bool, optional): Whether or not to strip the text when tokenizing. Defaults to `True` and removes excess spaces before and after the string. keep_accents (bool, optional): Whether or not to keep accents when tokenizing. Defaults to `False` and **does not** keep accents. bos_token (str, optional): A special token representing the beginning of a sequence that was used during pretraining. Defaults to `"<s>"`. eos_token (str, optional): A special token representing the end of a sequence that was used during pretraining. Defaults to `"</s>"`. unk_token (str, optional): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to `"<unk>"`. sep_token (str, optional): A special token separating two different sentences in the same input. Defaults to `"<sep>"`. pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to `"<pad>"`. cls_token (str, optional): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to `"<cls>"`. mask_token (str, optional): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to `"<mask>"`. additional_special_tokens (List[str], optional): A list of additional special tokens to be used by the tokenizer. Defaults to `["<eop>", "<eod>"]`. Attributes: sp_model (SentencePieceProcessor): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ resource_files_names = {"vocab_file": "spiece.model"} pretrained_resource_files_map = { "vocab_file": { "xlnet-base-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-base-cased-spiece.model", "xlnet-large-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-large-cased-spiece.model", "chinese-xlnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-base-spiece.model", "chinese-xlnet-mid": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-mid-spiece.model", "chinese-xlnet-large": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-large-spiece.model", } } pretrained_init_configuration = { "xlnet-base-cased": { "do_lower_case": False }, "xlnet-large-cased": { "do_lower_case": False }, "chinese-xlnet-base": { "do_lower_case": False }, "chinese-xlnet-mid": { "do_lower_case": False }, "chinese-xlnet-large": { "do_lower_case": False }, } pretrained_positional_embedding_sizes = { "xlnet-base-cased": None, "xlnet-large-cased": None, "chinese-xlnet-base": None, "chinese-xlnet-mid": None, "chinese-xlnet-large": None, } max_model_input_sizes = pretrained_positional_embedding_sizes padding_side = "left" pad_token_type_id = 3 def __init__(self, vocab_file, do_lower_case=False, remove_space=True, keep_accents=False, bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>", pad_token="<pad>", cls_token="<cls>", mask_token="<mask>", additional_special_tokens=["<eop>", "<eod>"], sp_model_kwargs=None, **kwargs): # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance( mask_token, str) else mask_token self._build_special_tokens_map_extended(mask_token=mask_token) self._pad_token_type_id = 3 self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) @property def vocab_size(self): return len(self.sp_model)
[文档] def get_vocab(self): vocab = { self.convert_ids_to_tokens(i): i for i in range(self.vocab_size) } vocab.update(self.added_tokens_encoder) return vocab
def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def preprocess_text(self, inputs): if self.remove_space: outputs = " ".join(inputs.strip().split()) else: outputs = inputs outputs = outputs.replace("``", '"').replace("''", '"') if not self.keep_accents: outputs = unicodedata.normalize("NFKD", outputs) outputs = "".join( [c for c in outputs if not unicodedata.combining(c)]) if self.do_lower_case: outputs = outputs.lower() return outputs def _tokenize(self, text): """Tokenize a string.""" text = self.preprocess_text(text) pieces = self.sp_model.encode(text, out_type=str) new_pieces = [] for piece in pieces: if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace( SPIECE_UNDERLINE, "")) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][ 0] == SPIECE_UNDERLINE: if len(cur_pieces[0]) == 1: cur_pieces = cur_pieces[1:] else: cur_pieces[0] = cur_pieces[0][1:] cur_pieces.append(piece[-1]) new_pieces.extend(cur_pieces) else: new_pieces.append(piece) return new_pieces def _convert_token_to_id(self, token): """Converts a token (str) to an id using the vocab. """ return self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) to a token (str) using the vocab.""" return self.sp_model.IdToPiece(index)
[文档] def convert_tokens_to_string(self, tokens): # Converts a sequence of tokens (strings for sub-words) in a single string. out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string
[文档] def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair (bool, optional): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. Returns: int: Number of tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None))
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Builds model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLNet sequence has the following format: - single sequence: ``X <sep> <cls>`` - pair of sequences: ``A <sep> B <sep> <cls>`` Args: token_ids_0 (List[int]): List of IDs for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for the second sequenze. Defaults to `None`. Returns: List[int]: List of input IDs with the appropriate special tokens. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return token_ids_0 + sep + cls return token_ids_0 + sep + token_ids_1 + sep + cls
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Builds offset map from a pair of offset map by concatenating and adding offsets of special tokens. An XLNet offset_mapping has the following format: - single sequence: ``X (0,0) (0,0)`` - pair of sequences: ``A (0,0) B (0,0) (0,0)`` Args: offset_mapping_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Defaults to `None`. Returns: List[tuple]: A list of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return offset_mapping_0 + [(0, 0)] + [(0, 0)] return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0) ] + [(0, 0)]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Creates a special tokens mask from the input sequences. This method is called when adding special tokens using the tokenizer `encode` method. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of `inputs_ids` for the second sequence. Defaults to `None`. already_has_special_tokens (bool, optional): Whether or not the token list already contains special tokens for the model. Defaults to `False`. Returns: List[int]: A list of integers which is either 0 or 1: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list( map( lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1] return ([0] * len(token_ids_0)) + [1, 1]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a token_type mask from the input sequences. If `token_ids_1` is not `None`, then a sequence pair token_type mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 | first sequence | second sequence | Else if `token_ids_1` is `None`, then a single sequence token_type mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 | first sequence | - 0 stands for the segment id of **first segment tokens**, - 1 stands for the segment id of **second segment tokens**, - 2 stands for the segment id of **cls_token**. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of `inputs_ids` for the second sequence. Defaults to `None`. Returns: List[int]: List of token type IDs according to the given sequence(s). """ sep = [self.sep_token_id] cls_segment_id = [2] if token_ids_1 is None: return len(token_ids_0 + sep) * [0] + cls_segment_id return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
[文档] def save_resources(self, save_directory): for name, file_name in self.resource_files_names.items(): save_path = os.path.join(save_directory, file_name) if os.path.abspath(self.vocab_file) != os.path.abspath( save_path) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, save_path) elif not os.path.isfile(self.vocab_file): with open(save_path, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto( ) fi.write(content_spiece_model)